colin
/
onnx-mlir
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'master' into enable-reciprocal

This commit is contained in:
Gheorghe-Teodor Bercea 2020-01-22 17:53:28 -05:00 committed by GitHub
parent ff0e25fdc9 e64c63b07e
commit 0ae8a0f23c
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 321 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
src/dialect/onnx/onnx.td
View File

@ -104,7 +104,7 @@ def ONNXGemmNoBiasOp: ONNX_Op<"GemmNoBias",
}
def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
[NoSideEffect]> {
[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
let summary = "ONNX Conv operation with no Bias operand.";
let description = [{
"The convolution operator consumes an input tensor and a filter, and"
@ -112,6 +112,8 @@ def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
}];
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W);
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
let verifier = [{ return ::verify(*this); }];
}
def ONNXMaxPoolSingleOutOp: ONNX_Op<"MaxPoolSingleOut",

177
src/dialect/onnx/onnx_ops.cpp
View File

@ -412,7 +412,7 @@ void ONNXReshapeOp::inferShapes() {
void ONNXTransposeOp::inferShapes() {
// Cannot infer shape if no shape exists.
if (!getOperand().getType().isa<RankedTensorType>())
emitError("Shape tensor not ranked.");
return;
// Naive transposition which handles the default case of
// reversing the shape of the tensor (similar to numpy.transpose).
@ -448,6 +448,181 @@ LogicalResult verify(ONNXTransposeOp op) {
return success();
}
//===----------------------------------------------------------------------===//
// Conv
// For this operation, we define the attributes once in the original Conv
// operation class. There is no need to redefine the attribute names for the
// other classes based on Conv.
void ONNXConvNoBiasOp::inferShapes() {
// Generic shape for data input X and weight tensor W:
// X: (N x C x D1 x D2 ... x Dn)
// W: (M x C/group x k1 x k2 x ... x kn)
// Cannot infer shape if no shape exists.
if (!getOperand(0).getType().isa<RankedTensorType>() ||
!getOperand(1).getType().isa<RankedTensorType>())
return;
auto dataTy = getOperand(0).getType().cast<RankedTensorType>();
auto weightTy = getOperand(1).getType().cast<RankedTensorType>();
auto dataShape = dataTy.getShape();
auto weightShape = weightTy.getShape();
// Check that shape of weight and data have same length.
if (dataShape.size() != weightShape.size())
emitError("Weight size not compatible with data size.");
// Required attribute auto_pad defaults to NOTSET.
auto autoPad = getAttrOfType<StringAttr>(
ONNXConvOp::getAutoPadAttrName()).getValue();
// Group is a required attribute and should have default value of 1.
int64_t group = getAttrOfType<IntegerAttr>(
ONNXConvOp::getGroupAttrName()).getInt();
// Check that the X.shape[1] == (W.shape[1] * group) == C condition holds.
if (dataShape[1] != (weightShape[1] * group))
emitError("Channel dimension mismatch.");
// Note: the value of the group attribut only impacts the way the
// computation is carried out and not the actual output size.
// First two output dimensions consist of the number of batches and the
// number of kernels being applied.
//
SmallVector<int64_t, 2> dims;
// Insert batch size.
dims.emplace_back(dataShape[0]);
// Insert number of filters being applied (number of output channels).
dims.emplace_back(weightShape[0]);
// Spatial dimensions of the output are computed using the formula:
//
// dim = (inputDim - kernelDim + startPadding + endPadding) / stride + 1
//
SmallVector<int64_t, 2> outSpatialDims;
// Number of spatial dimensions.
int32_t nDims = dataShape.size() - 2;
// Initialize dimenions based on the input spatial dimensions.
for (int i = 2; i < dataShape.size(); ++i)
outSpatialDims.emplace_back(dataShape[i]);
// Use kernel_shape attribute if present otherwise use size from weight
// argument.
SmallVector<int64_t, 2> kernelDims;
if (auto kernelShape = getAttrOfType<ArrayAttr>(
ONNXConvOp::getKernelShapeAttrName())) {
if (kernelShape.getValue().size() != nDims)
emitError("kernel_shape length incompatible with spatial dimensions.");
for (int i = 0; i < nDims; ++i)
kernelDims.emplace_back(
(kernelShape.getValue()[i]).cast<IntegerAttr>().getInt());
} else {
for (int i = 0; i < nDims; ++i)
kernelDims.emplace_back(weightShape[i + 2]);
}
// Check if dilations attribute is present.
// If it is then compute new kernel size that includes the receptive field.
// In this calculation we assume that the receptive field pixels must all be
// within the bounds of the image. In this case the new kernel size is given
// by:
//
// ( K + 1 ) * d - 1
// where K is a kernel dimension and d is the dilation along that axis.
//
// From a dimensionality perspective the kernel size becomes the dilated
// kernel size.
if (auto dilations = getAttrOfType<ArrayAttr>(
ONNXConvOp::getDilationsAttrName())) {
if (dilations.getValue().size() != nDims)
emitError("dilations length incompatible with spatial dimensions.");
for (int i = 0; i < nDims; ++i)
kernelDims[i] = (kernelDims[i] + 1) *
(dilations.getValue()[i]).cast<IntegerAttr>().getInt() - 1;
}
// Subtract kernel dimensions from input data dimensions.
for (int i = 0; i < nDims; ++i)
outSpatialDims[i] -= kernelDims[i];
// Add padding information.
if (autoPad == "NOTSET") {
// Use pads to to determine the padding. If attribute is not
// present then pads is considered to be all zeros (no padding).
if (auto pads = getAttrOfType<ArrayAttr>(
ONNXConvOp::getPadsAttrName())) {
// pads consists of two entries for each spatial axis.
if (pads.getValue().size() != 2 * nDims)
emitError("pads size is not twice the spatial size.");
for (int i = 0; i < nDims; ++i) {
// Padding for beginning of axis.
int32_t p = (pads.getValue()[i]).cast<IntegerAttr>().getInt();
outSpatialDims[i] += p;
// Padding for end of axis.
p = (pads.getValue()[i + nDims]).cast<IntegerAttr>().getInt();
outSpatialDims[i] += p;
}
}
} else if (autoPad == "SAME_UPPER" || autoPad == "SAME_LOWER") {
// Pad input so that output size matches input size.
// Each spatial dimension needs to be padded by a total of:
//
// K - 1
//
// where K is a kernel spatial dimension.
// Pad as if stride is 1.
for (int i = 0; i < nDims; ++i)
outSpatialDims[i] += kernelDims[i] - 1;
} else if (autoPad == "VALID") {
// No padding
} else {
emitError("Unexpected attribute value for auto_pad.");
}
// Strides
if (auto strides = getAttrOfType<ArrayAttr>(
ONNXConvOp::getStridesAttrName())) {
if (strides.getValue().size() != nDims)
emitError("strides length incompatible with spatial dimensions.");
for (int i = 0; i < nDims; ++i) {
int64_t stride =
(strides.getValue()[i]).cast<IntegerAttr>().getInt();
outSpatialDims[i] = floor(outSpatialDims[i] / stride);
}
}
for (int i = 0; i < nDims; ++i)
outSpatialDims[i] += 1;
dims.append(outSpatialDims.begin(), outSpatialDims.end());
getResult().setType(RankedTensorType::get(dims, dataTy.getElementType()));
}
LogicalResult verify(ONNXConvNoBiasOp op) {
auto module = op.getParentOfType<ModuleOp>();
if (!module)
op.emitError("expected to belong to a module");
auto autoPadAttr = op.getAttrOfType<StringAttr>(
ONNXConvOp::getAutoPadAttrName());
if (!autoPadAttr)
op.emitError("auto_pad attribute not specified.");
if (autoPadAttr.getValue() != "NOTSET")
if (auto pads = op.getAttrOfType<ArrayAttr>(
ONNXConvOp::getPadsAttrName()))
op.emitError("auto_pad and pads are both set.");
auto groupAttr =
op.getAttrOfType<IntegerAttr>(ONNXConvOp::getGroupAttrName());
if (!groupAttr)
op.emitError("group attribute not specified.");
return success();
}
//===----------------------------------------------------------------------===//
// TableGen'd op method definitions
//===----------------------------------------------------------------------===//

9
src/dialect/onnx/onnxop.inc
View File

@ -324,6 +324,15 @@ def ONNXConvOp:ONNX_Op<"Conv",
}];
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W, AnyTypeOf<[AnyMemRef, AnyTensor]>:$B);
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
let extraClassDeclaration = [{
static StringRef getAutoPadAttrName() { return "auto_pad"; }
static StringRef getDilationsAttrName() { return "dilations"; }
static StringRef getGroupAttrName() { return "group"; }
static StringRef getKernelShapeAttrName() { return "kernel_shape"; }
static StringRef getPadsAttrName() { return "pads"; }
static StringRef getStridesAttrName() { return "strides"; }
}];
}
def ONNXConvIntegerOp:ONNX_Op<"ConvInteger",

3
src/pass/shape_inference_pass.cpp
View File

@ -117,7 +117,8 @@ public:
op->getName().getStringRef() != "onnx.GemmNoBias" &&
op->getName().getStringRef() != "onnx.Reshape" &&
op->getName().getStringRef() != "onnx.Transpose" &&
op->getName().getStringRef() != "onnx.Softmax")
op->getName().getStringRef() != "onnx.Softmax" &&
op->getName().getStringRef() != "onnx.ConvNoBias")
return false;
return llvm::any_of(op->getResultTypes(), [](Type result_type) {
return !result_type.isa<RankedTensorType>();

133
test/mlir/onnx/onnx_shape_inference.mlir
View File

@ -1,7 +1,10 @@
// RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
//===----------------------------------------------------------------------===//
/// Test the default behavior of transpose when no information for the
/// permutation of the axes is provided.
/// permutation of the axes is provided and when a permutation is provided.
//===----------------------------------------------------------------------===//
func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
%0 = "onnx.Transpose"(%arg0) : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
@ -12,6 +15,7 @@ func @test_default_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
// CHECK: return [[RES]] : tensor<32x1x5x5xf32>
/// Test shape inference for transposition when perm attribute is specified.
func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
%0 = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
@ -19,4 +23,129 @@ func @test_transpose(%arg0 : tensor<5x5x1x32xf32>) -> tensor<*xf32> {
// CHECK-LABEL: test_transpose
// CHECK: [[RES_ATTR:%.+]] = "onnx.Transpose"(%arg0) {perm = [2, 0, 3, 1]} : (tensor<5x5x1x32xf32>) -> tensor<1x5x32x5xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x5xf32>
//===----------------------------------------------------------------------===//
/// Test shape inference for ConvNoBias operation and all its attributes.
//===----------------------------------------------------------------------===//
/// Default and required attributes.
func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_1
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
/// kernel_shape attribute.
func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_2
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
/// pads attribute.
/// Use pads to make output size equal to input size by adding K - 1 to the result.
func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_3
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
/// auto_pad set to SAME_UPPER and SAME_LOWER.
func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_4
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_5
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
/// auto_pad set to VALID.
func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_6
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
/// With strides attribute.
func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_7
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
/// auto_pad set to SAME_UPPER with strides attribute.
/// The auto_pad will pas as if stride is equal to 1.
func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_8
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
/// dilations attribute.
func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_9
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
/// dilations attribute with stride.
func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_10
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
/// dilations attribute with auto_pad set to SAME_UPPER.
func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
"std.return"(%0) : (tensor<*xf32>) -> ()
}
// CHECK-LABEL: test_conv_no_bias_11
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>